---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
- bleu
model-index:
- name: wav2vec2-mms-1b-CV17.0
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: yo
      split: test
      args: yo
    metrics:
    - name: Wer
      type: wer
      value: 0.6538388264431321
    - name: Bleu
      type: bleu
      value: 0.14202013774436864
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# wav2vec2-mms-1b-CV17.0

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6919
- Wer: 0.6538
- Cer: 0.2510
- Bleu: 0.1420

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    | Bleu   |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|:------:|
| 6.2938        | 3.0769  | 200  | 3.8350          | 0.9981 | 0.9092 | 0.0    |
| 2.0522        | 6.1538  | 400  | 0.7219          | 0.6997 | 0.2730 | 0.1116 |
| 0.7043        | 9.2308  | 600  | 0.7137          | 0.7419 | 0.2682 | 0.0933 |
| 0.6497        | 12.3077 | 800  | 0.6962          | 0.6664 | 0.2667 | 0.1318 |
| 0.614         | 15.3846 | 1000 | 0.6680          | 0.6586 | 0.2596 | 0.1356 |
| 0.5794        | 18.4615 | 1200 | 0.6798          | 0.6722 | 0.2599 | 0.1254 |
| 0.5439        | 21.5385 | 1400 | 0.6724          | 0.6665 | 0.2541 | 0.1287 |
| 0.5146        | 24.6154 | 1600 | 0.6906          | 0.6704 | 0.2513 | 0.1327 |
| 0.489         | 27.6923 | 1800 | 0.6886          | 0.6599 | 0.2509 | 0.1390 |
| 0.4668        | 30.7692 | 2000 | 0.6919          | 0.6538 | 0.2510 | 0.1420 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1